FRPO/qwen3-1.7b-a14_shuffle-k1-cNone-shuf-clip0.2-mb4-eta100-bs256x5-n2-seed1
FRPO/qwen3-1.7b-a14_shuffle-k1-cNone-shuf-clip0.2-mb4-eta100-bs256x5-n2-seed1 is a 1.7 billion parameter language model based on the Qwen3 architecture, specifically an RL fine-tuned checkpoint from the KL-in-LLM-RL / FRPO experiments. This model was trained using the verl framework and is derived from the Qwen/Qwen3-1.7B base model. It features fp32 safetensor weights and has a context length of 32768 tokens, making it suitable for tasks benefiting from reinforcement learning optimization.
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Model Overview
FRPO/qwen3-1.7b-a14_shuffle-k1-cNone-shuf-clip0.2-mb4-eta100-bs256x5-n2-seed1 is a 1.7 billion parameter language model that has undergone Reinforcement Learning (RL) fine-tuning. It is built upon the Qwen/Qwen3-1.7B base model and is a product of the KL-in-LLM-RL / FRPO experimental series, utilizing the verl training framework.
Key Characteristics
- Base Model: Qwen3-1.7B architecture.
- Fine-tuning: RL fine-tuned checkpoint from specific KL-in-LLM-RL / FRPO experiments.
- Training Framework: Developed using the
verlframework. - Weights: Provided in fp32 safetensors format, directly as saved by the trainer without further processing.
- Context Length: Supports a substantial context window of 32768 tokens.
Potential Use Cases
This model is particularly relevant for researchers and developers interested in:
- Exploring the effects of Reinforcement Learning (RL) fine-tuning on Qwen3-1.7B.
- Experimenting with models derived from the KL-in-LLM-RL / FRPO research.
- Applications requiring a model with a 32K context length and RL-based optimizations.